WFC3 Space Telescope Analysis Newsletter - Issue 13, March 2013

Contents

Alpha Release of the Empirical CTE Model

This brief article is intended to introduce the alpha version of the pixel-based charge-transfer efficiency (CTE) correction for WFC3/UVIS. The UVIS model uses a very similar algorithm to the one that is currently a part of the ACS/WFC pipeline and is described in Anderson & Bedin (2010 PASP 122 1035). We find that with some minor modifications and additional constraints, the same model does a reasonable job treating charge transfer inefficiency for UVIS as well.

This model is not yet a part of the UVIS pipeline, but we wanted to make it available to the community as quickly as possible, so we are releasing it as a stand-alone FORTRAN program. The routine operates on the "raw" images and produces either corrected raw images ("rac" files) or uses the derived correction to fix the "flt" images and produce "flc" images, analogous to what the ACS pipeline produces. These "flc" images can be analyzed or astrodrizzle-combined like normal "flt" images.

A website has been set up to serve the code and describe its basic operation. Since this is an "alpha" release, users should be aware that the code could change significantly before it is stable and ready to be implemented in the CAL/WF3 pipeline. At the bottom of the webpage, users are encouraged to join a majordomo mailing list that will update them on changes or issues have been found with the software. We encourage all who download the software to join this list.

Finally, a caveat: It is worth emphasizing that inefficient CTE results in a blurring of the original pixel distribution, which means that the pixel-based CTE reconstruction is necessarily a deconvolution process. When CTE losses are perturbations on the original distribution, the reconstruction can be done quite accurately. But when losses become significant, it becomes much harder to achieve an accurate deconvolution. This is particularly problematic when we are dealing with faint sources on low backgrounds. An additional complication of reconstruction is that readnoise gets added after the electrons have already undergone their leaky journey to the register. As such, the images we analyze have some pixel-to-pixel variations that are not caused by CTE issues, superposed on the CTE-related variations on which we are trying to base the reconstruction. Because of this added readnoise, our restoration algorithm must necessarily be conservative in terms of what variations it treats as signal and what it treats as readnoise. The result is that the model often under-corrects faint sources on low backgrounds.